1 | P a g e

FIFA 17 ANALYSIS

WITH MICROSOFT

EXCEL

- HANSEL NICHOLAS D’SOUZA

2 | P a g e

INDEX

1. INTRODUCTION TO THE DATA……………………………………..3

2. SORTING PLAYERS BY COUNTRY & CLUB………………………4

3. ANALYSIS BY PLAYER AGE…………………………………………….6

4. ANALYSIS BY PLAYER RATINGS…………………………………….8

5. REFERENCES………………………………………………………………..13

3 | P a g e

Section 1 : Introduction to the Data

The data set that I have worked on to perform the following analysis is based on the

popular video game FIFA. The following extract best describes the product :

“FIFA, also known as FIFA Football or FIFA Soccer, is a series of association

football video games or football simulator, released annually by Electronic Arts under the EA

Sports label. Football video games such as Sensible Soccer, Kick Off and Match Day had been

developed since the late 1980s and already competitive in the games market when EA Sports

announced a football game as the next addition to their EA Sports label.” – Wikipedia

FIFA 17 is the 2017 edition of the global franchise FIFA, that mimics and simulates the

experience of playing and managing a professional soccer team. The game uses the

attributes, likeness and statistics of real soccer players.

Each column in the table represents an individual attribute of the players and each

record represents one of the 17,589 stock soccer players in the game. Each player has a

preferred position which can basically be classified into one of the four primary

positions: Forward, Midfielder, Defender, Goalkeeper.

Below are each of the columns of the data set and their type of measurement scale:

Name (Categorical) Reactions (Ratio) GK_Kicking (Ratio) Nationality (Categorical) Attacking_Position (Ratio) GK_Handling (Ratio) National_Position (Categorial) Interceptions (Ratio) GK_Reflexes (Ratio) National_Kit (Interval) Vision (Ratio) Club (Categorical) Composure (Ratio) Club_Position (Categorical) Crossing (Ratio) Club_Kit (Interval) Short_Pass (Ratio) Club_Joining (Interval) Long_Pass Ratio) Contract_Expiry (Interval) Acceleration (Ratio) Rating (Ratio) Speed (Ratio) Height (Ratio) Stamina (Ratio) Weight (Ratio) Strength (Ratio) Prefered_Foot (Ratio) Balance (Ratio) Birth_Date (Interval) Agility (Ratio) Age (Ratio) Jumping (Ratio) Prefered_Position (Categorical) Heading (Ratio) Work_Rate (Ordinal) Shot_Power (Ratio) Weak_foot (Ratio) Finishing (Ratio) Skill_Moves (Ratio) Long_Shot (Ratio) Ball_Control (Ratio) Curve (Ratio) Dribbling (Ratio) Freekick_Accuracy (Ratio) Marking (Ratio) Penalties (Ratio) Sliding_Tackle (Ratio) Volleys (Ratio) Standing_Tackle (Ratio) GK_Position (Ratio) Aggression (Ratio) GK_Diving (Ratio)

4 | P a g e

Section 2: Sorting Players by Club & Country

The first, and perhaps most obvious ways to groups the players is by the Club that they

play for. To obtain a general “ranking” of the clubs, I proceeded with obtaining the count

of the number of players playing for the club, their cumulative Player Ratings and then

finding the Average Player rating per club. The results are as below:

As observed above, Juventus lie in first place closely followed by FC Bayern in 2nd place

and then the other 8. Below is a column chart of the above data.

Next, I will proceed to rank the top 100 players in the game based on overall player

rating and segregate them by country. To view which countries have the most players in

the top 100 of the game, I created a stacked column as shown below of the top 10

countries, and as shown, Spain leads the pack with 16 players or 16% of the top 100

players.

5 | P a g e

Another way to segregate the data is by representing the total number of players on a

world map by Country such as below;

This map, an excel heat map, shows the distribution of players around the world. The

regions in green, such as Brazil, Argentina and Spain signify a dense population of

players and the regions in red a least.

6 | P a g e

Section 3: Analysis by Age

The next step in my analysis would involve sorting and analyzing the players data based

on their ages.

The graph below shows the distribution of players ages vs the density of players. As

observed by the scatter chart below, a vast majority of players, or approximately 8% of

the players are 25 years old.

Another variable would be to consider the age of which a player is at his peak. Suppose I

wanted to view the age at which players ‘peak’, or in FIFA terms, the age at which most

players have a higher overall rating.

7 | P a g e

The data as shown above in the clustered chart and the line chart, shows a higher

concentration of highly rated players between the ages of 24-32, below and above which

there is a noticeable drop in the overall player rating. This would imply that most of the

higher rating players lie between the ages of 24-32.

8 | P a g e

Section 4: Analysis by Ratings

Lets begin the analysis in this section by using heights of players as a parameter. Taking

all the heights and using a box and whisker plot, we get the following chart;

The very first thing we notice are the outliers. With the values of Q1, Q2 and Q3 of the

Box Plot being values being 176, 181 and 186 cms respectively, these values lying below

Q1 and above Q3, such as 207cm and 155cm constitute extreme values or outliers.

9 | P a g e

Another useful way of viewing the

general statistics of player data is to

run a descriptive statistic of the overall

rating of the players as shown to the

left.

We see that the mean of the 1,163,731

player ratings is approximately 66 and

so is the Median. The most frequent

rating is 67, while the range is between

94 and 45, or 49. The data has

negative skewness, which means the

data tends to tails off to the left and is

fairly symmetrical. The negative

kurtosis of -0.02 , signifies a

platykurtic or light tailed distribution.

To verify the Empirical rules, we first

calculate Mean +/- 3 S.D , which

equals 87.4 and 44.92 respectively.

Filtering the data based on this constraint yields 17,555 records out of a possible 17,588

records, or 99.81% of data. This verifies the empirical rule that 99.7% of the data lies

between the ranges stated above.

The Coefficient of Variation(CV) is calculated to be 0.107

Next up, I wanted to get a general view of the key stats of the top 10 players in the game.

This could be achieved easily by viewing a radar chart as shown below.

10 | P a g e

The chart above shows a general view of viewing multivariate data of each players key

stats on the scale of 1-100. It is pretty evident from the data that Goal keepers such as

Neuer, Courtois and De Gea have a much higher Goal keeper reflexes stat and

consequently lower numbers in the other departments, while speedsters such as Bale

and Neymar excel in the dribbling and Speed stats. As noticed, there are defenders in

the top 10, hence the lower Standing tackle numbers.

Another useful way of viewing data is by using the color scales, which shades the cells

based on the numerical value present using a color palette. As shown above, I have used

a red-yellow-green palette to visually represent how high a certain stat of a player is,

with red showing considerably low numbers while green displays higher stats. Players

show higher numbers in the stats that their playing position generally requires them to

specialize in. For example, defender Jerome Boateng displays a lot of the green shading

in the Defensive skills such as Sliding Tackle, standing tackle etc. and the same applies

to other players in their respective positions.

11 | P a g e

Speaking of position, another important way of grouping players is by the fundamental

primary position he plays in i.e, Goalkeeper, Defense, Midfield or Forward. Since each

player had a specialized position such as CAM(Central Attacking Midfield), LW(Left

Wing), RB(Right Back) etc. , a lookup table had to be created to categorize the positions

into one of the 4 primary positions.

The pie chart, as shown above demonstrates the overwhelming number of defenders

present in the game with a whopping 58% of the records. As expected, Goalkeepers only

consist of 6% of the data as every team consists of an average of 2-3 goalkeepers in a

squad of 32.

Another very sought after factor by soccer players, which provides another interesting

dimension of insight into the data set, is the Jersey Number. Most players seek to obtain

prestigious jersey numbers, and the following Area chart below shows the overall

distribution of data.

12 | P a g e

As observed above, the graph peaks at certain numbers, such as the ‘1’, ‘7’ and ‘8’ jerseys,

which signifies that most players prefer these Jersey numbers over the others.

We can finally also take a look at the correlation between individual attributes of the

players. A table of the following is displayed below:

As seen, there are some rather unsurprising findings here. For example, we see negative

correlations between Age VS Speed and Acceleration, showing that as a player gets

older, his overall pace decreases. Also, as Strength increases, Speed decreases showing

that the stronger players are generally slower.

The stronger positive correlations are between Ball Control VS Dribbling, showing that

having a higher Ball Control will lead to higher Dribbling stats, and Short Pass VS Long

Pass, showing that players excelling in the Long Pass tend to be equally good at the

Short Pass.

13 | P a g e

Section 5: References

Source of Data : kaggle.com

URL of Dataset : https://www.kaggle.com/artimous/complete-fifa-2017-player-dataset-global